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sustainable catalyst platforms. This PhD project, Data-enhanced Quantum Chemistry for Predictive Catalyst Design, aims to Develop computational approaches for the discovery of sustainable catalysts based on low
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quantum chemistry software capable of simulating molecules Ideally, prior experience with embedding methods or molecular crystals Excellent knowledge of written and spoken German and English Scientific
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Innovation in science, and in quantum-, nano- and bio-technologies depends on a rigorous understanding of the inner workings of custom-tailored materials. Since the elementary building blocks
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schools of Aalto University. It combines natural sciences and engineering in a unique way. The Department of Chemistry and Materials Science is looking for: A Doctoral Researcher (PhD student) in Machine
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14 Jul 2026 Job Information Organisation/Company AALTO UNIVERSITY Research Field Chemistry Engineering Computer science Mathematics Physics Researcher Profile Recognised Researcher (R2) First Stage
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in the field of machine learning for circular polyurethane design. In your work you will help to develop a quantum-to-plant model to predict polyurethane recyclability and separation energy from
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collaborate with other departments at Aalborg University, including Energy, Biotechnology and Chemistry. International collaboration is a core element of the project, and you will be part of joint activities
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. In my group, we synthesise these functional nanomaterials from the bottom-up, using protocols of molecular beam epitaxy and on-surface supramolecular chemistry. We study these systems by means
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new research pillars proposed by the Centre for Materials Science at QUT - quantum technology. The PhD will be supervised by Dr Meng Li and based at the School of Chemistry and Physics at QUT
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excitations and excitonic effects using advanced Wannier-based methods * Quantum transport in polymer materials with electron–phonon coupling Full details and application instructions: https://www.ch.nat.tum.de